Institutions per country
universities_per_country <- works %>%
distinct(country_code, institution_id) %>%
count(country_code, name = "n_universities") %>%
arrange(desc(n_universities)) %>%
collect() %>%
add_country()
universities_per_country %>%
select(country, n_universities) %>%
knitr::kable()
| United States |
6808 |
| India |
2061 |
| China |
1985 |
| United Kingdom |
1610 |
| Japan |
1444 |
| Germany |
1033 |
| France |
1024 |
| Brazil |
807 |
| Canada |
746 |
| Russian Federation |
665 |
| Spain |
642 |
| Italy |
582 |
| Australia |
538 |
| Korea, Rep. |
478 |
| NA |
433 |
| Indonesia |
407 |
| Poland |
333 |
| Netherlands |
329 |
| Switzerland |
293 |
| Taiwan, China |
264 |
| Turkiye |
240 |
| Pakistan |
240 |
| Mexico |
236 |
| Iran, Islamic Rep. |
234 |
| Norway |
219 |
| Colombia |
205 |
| Ukraine |
199 |
| Czech Republic |
195 |
| Sweden |
186 |
| Portugal |
181 |
| Austria |
177 |
| Nigeria |
171 |
| South Africa |
168 |
| Belgium |
158 |
| Thailand |
156 |
| Argentina |
151 |
| Greece |
145 |
| Finland |
144 |
| Denmark |
142 |
| Ireland |
138 |
| Bangladesh |
132 |
| Philippines |
122 |
| Malaysia |
121 |
| Israel |
120 |
| Hungary |
112 |
| New Zealand |
108 |
| Vietnam |
103 |
| Chile |
97 |
| Singapore |
97 |
| Egypt, Arab Rep. |
96 |
| Saudi Arabia |
91 |
| Slovak Republic |
89 |
| Kenya |
86 |
| Romania |
80 |
| Bulgaria |
80 |
| Peru |
78 |
| Slovenia |
76 |
| Ecuador |
65 |
| Uganda |
64 |
| United Arab Emirates |
60 |
| Serbia |
60 |
| Croatia |
59 |
| Ghana |
54 |
| Ethiopia |
54 |
| Iraq |
52 |
| Kazakhstan |
51 |
| Tanzania |
48 |
| Algeria |
47 |
| Nepal |
46 |
| Belarus |
40 |
| Venezuela, RB |
39 |
| Sri Lanka |
38 |
| Cuba |
38 |
| Latvia |
38 |
| Lithuania |
36 |
| Tunisia |
33 |
| Estonia |
33 |
| Jordan |
32 |
| Zimbabwe |
28 |
| Morocco |
28 |
| Sudan |
27 |
| Uruguay |
27 |
| Uzbekistan |
27 |
| Cameroon |
27 |
| Lebanon |
26 |
| Cyprus |
26 |
| Costa Rica |
25 |
| Luxembourg |
23 |
| Armenia |
23 |
| Georgia |
22 |
| Oman |
21 |
| Cambodia |
20 |
| Albania |
20 |
| Bolivia |
20 |
| Azerbaijan |
19 |
| Iceland |
19 |
| Kuwait |
19 |
| Qatar |
19 |
| Myanmar |
19 |
| Syrian Arab Republic |
19 |
| Bosnia and Herzegovina |
19 |
| Congo, Dem. Rep. |
18 |
| Paraguay |
17 |
| West Bank and Gaza |
17 |
| Zambia |
16 |
| Malawi |
15 |
| Dominican Republic |
15 |
| Panama |
15 |
| Moldova |
14 |
| Mozambique |
14 |
| Mongolia |
14 |
| Senegal |
14 |
| Kyrgyz Republic |
14 |
| Yemen, Rep. |
14 |
| North Macedonia |
13 |
| Bahrain |
13 |
| Guatemala |
13 |
| Burkina Faso |
13 |
| Rwanda |
12 |
| Hong Kong SAR, China |
11 |
| Afghanistan |
10 |
| Cote d'Ivoire |
10 |
| Botswana |
10 |
| Libya |
10 |
| Nicaragua |
9 |
| Tajikistan |
9 |
| El Salvador |
9 |
| Trinidad and Tobago |
8 |
| Somalia |
8 |
| Angola |
7 |
| Namibia |
7 |
| Madagascar |
7 |
| Papua New Guinea |
7 |
| Mali |
6 |
| Niger |
6 |
| Mauritius |
6 |
| Malta |
6 |
| Gambia, The |
5 |
| Benin |
5 |
| Lao PDR |
5 |
| Montenegro |
5 |
| Fiji |
5 |
| Honduras |
5 |
| Kosovo |
5 |
| Jamaica |
5 |
| Brunei Darussalam |
4 |
| Gabon |
4 |
| Korea, Dem. People's Rep. |
4 |
| Curacao |
4 |
| Togo |
4 |
| South Sudan |
4 |
| Guinea-Bissau |
3 |
| Bahamas, The |
3 |
| Liechtenstein |
3 |
| Belize |
3 |
| Puerto Rico |
3 |
| Sierra Leone |
3 |
| Bermuda |
3 |
| French Polynesia |
3 |
| Mauritania |
3 |
| Greenland |
3 |
| Turkmenistan |
3 |
| Liberia |
3 |
| Monaco |
3 |
| Maldives |
3 |
| St. Kitts and Nevis |
3 |
| NA |
2 |
| Antigua and Barbuda |
2 |
| Guyana |
2 |
| Haiti |
2 |
| Seychelles |
2 |
| Suriname |
2 |
| Congo, Rep. |
2 |
| Sint Maarten (Dutch part) |
2 |
| New Caledonia |
2 |
| St. Lucia |
2 |
| NA |
2 |
| Macao SAR, China |
2 |
| Eswatini |
2 |
| Bhutan |
2 |
| Chad |
2 |
| Burundi |
2 |
| NA |
1 |
| Aruba |
1 |
| Guinea |
1 |
| Grenada |
1 |
| Palau |
1 |
| NA |
1 |
| Timor-Leste |
1 |
| British Virgin Islands |
1 |
| Faroe Islands |
1 |
| St. Vincent and the Grenadines |
1 |
| Isle of Man |
1 |
| Sao Tome and Principe |
1 |
| Andorra |
1 |
| NA |
1 |
| Barbados |
1 |
| NA |
1 |
| NA |
1 |
| Cabo Verde |
1 |
| Cayman Islands |
1 |
| Eritrea |
1 |
| NA |
1 |
| Marshall Islands |
1 |
| Micronesia, Fed. Sts. |
1 |
| Gibraltar |
1 |
| Lesotho |
1 |
| NA |
1 |
| Central African Republic |
1 |
# papers per country
papers_per_country <- works %>%
distinct(country_code, id, work_frac, author_position, institution_id) %>%
group_by(country_code) %>%
summarise(sum_fractional_works = sum(work_frac) %>% round(digits = 1)) %>%
arrange(desc(sum_fractional_works)) %>%
collect() %>%
add_country()
papers_per_country %>%
select(country, country_code, sum_fractional_works) %>%
knitr::kable()
| United States |
US |
747996.2 |
| China |
CN |
595755.7 |
| Brazil |
BR |
400590.8 |
| United Kingdom |
GB |
194942.3 |
| Germany |
DE |
170473.8 |
| Japan |
JP |
148536.0 |
| Spain |
ES |
120279.7 |
| India |
IN |
118094.2 |
| Canada |
CA |
115456.2 |
| Italy |
IT |
104839.5 |
| Korea, Rep. |
KR |
103472.3 |
| Australia |
AU |
100722.0 |
| France |
FR |
89289.2 |
| Netherlands |
NL |
61987.0 |
| Taiwan, China |
TW |
58803.5 |
| Poland |
PL |
58718.3 |
| Indonesia |
ID |
50570.5 |
| Iran, Islamic Rep. |
IR |
48697.3 |
| Sweden |
SE |
48538.7 |
| Switzerland |
CH |
42420.7 |
| Mexico |
MX |
40329.8 |
| Russian Federation |
RU |
37819.3 |
| South Africa |
ZA |
37177.8 |
| Malaysia |
MY |
36006.3 |
| Turkiye |
TR |
32717.7 |
| Belgium |
BE |
32310.3 |
| Colombia |
CO |
29179.2 |
| Denmark |
DK |
29004.8 |
| Portugal |
PT |
27315.5 |
| Norway |
NO |
26270.0 |
| Austria |
AT |
21768.8 |
| Argentina |
AR |
21129.7 |
| Pakistan |
PK |
20774.2 |
| Czech Republic |
CZ |
20532.3 |
| Saudi Arabia |
SA |
20301.3 |
| Israel |
IL |
20143.3 |
| Finland |
FI |
19189.3 |
| Chile |
CL |
18898.0 |
| Singapore |
SG |
17013.7 |
| Egypt, Arab Rep. |
EG |
16972.7 |
| Thailand |
TH |
15408.0 |
| Greece |
GR |
13735.5 |
| New Zealand |
NZ |
13046.3 |
| Nigeria |
NG |
13013.3 |
| Ireland |
IE |
12550.7 |
| Ukraine |
UA |
10561.5 |
| Hungary |
HU |
10517.8 |
| Serbia |
RS |
9506.3 |
| NA |
NA |
9483.0 |
| Ethiopia |
ET |
9428.2 |
| Romania |
RO |
7396.5 |
| Croatia |
HR |
7322.8 |
| Slovak Republic |
SK |
7011.0 |
| Bangladesh |
BD |
6685.3 |
| Slovenia |
SI |
6396.3 |
| Peru |
PE |
6205.5 |
| Morocco |
MA |
5027.8 |
| Ghana |
GH |
4885.5 |
| Bahrain |
BH |
4004.8 |
| Lithuania |
LT |
3961.0 |
| Ecuador |
EC |
3926.5 |
| Bulgaria |
BG |
3555.0 |
| Kenya |
KE |
3518.5 |
| Vietnam |
VN |
3455.3 |
| Nepal |
NP |
3416.3 |
| Costa Rica |
CR |
3380.8 |
| United Arab Emirates |
AE |
3175.5 |
| Sri Lanka |
LK |
2985.8 |
| Uganda |
UG |
2887.3 |
| Estonia |
EE |
2875.0 |
| Iraq |
IQ |
2854.2 |
| Lebanon |
LB |
2848.5 |
| Uruguay |
UY |
2814.8 |
| Qatar |
QA |
2813.7 |
| Jordan |
JO |
2812.8 |
| Tunisia |
TN |
2727.0 |
| Philippines |
PH |
2368.5 |
| Cameroon |
CM |
2285.5 |
| Tanzania |
TZ |
2216.7 |
| Venezuela, RB |
VE |
1633.7 |
| Cyprus |
CY |
1630.2 |
| Oman |
OM |
1593.0 |
| Kuwait |
KW |
1584.2 |
| Benin |
BJ |
1381.8 |
| Armenia |
AM |
1364.0 |
| Hong Kong SAR, China |
HK |
1261.2 |
| Mozambique |
MZ |
1257.3 |
| Bosnia and Herzegovina |
BA |
1202.7 |
| Latvia |
LV |
1190.7 |
| Luxembourg |
LU |
1142.5 |
| Iceland |
IS |
1110.3 |
| Cuba |
CU |
1097.3 |
| Algeria |
DZ |
1059.3 |
| Zimbabwe |
ZW |
1017.7 |
| Kazakhstan |
KZ |
999.8 |
| Puerto Rico |
PR |
958.5 |
| Sudan |
SD |
897.5 |
| Antigua and Barbuda |
AG |
881.7 |
| Jamaica |
JM |
881.0 |
| West Bank and Gaza |
PS |
879.7 |
| Zambia |
ZM |
706.8 |
| Belarus |
BY |
668.5 |
| Paraguay |
PY |
634.5 |
| Cambodia |
KH |
623.5 |
| Lao PDR |
LA |
621.5 |
| El Salvador |
SV |
566.0 |
| Malawi |
MW |
553.0 |
| Syrian Arab Republic |
SY |
551.8 |
| Panama |
PA |
550.8 |
| Malta |
MT |
506.0 |
| Senegal |
SN |
480.5 |
| Uzbekistan |
UZ |
467.0 |
| Botswana |
BW |
450.0 |
| Kosovo |
XK |
422.5 |
| Azerbaijan |
AZ |
383.3 |
| North Macedonia |
MK |
375.8 |
| Burkina Faso |
BF |
370.0 |
| Bolivia |
BO |
368.3 |
| Montenegro |
ME |
365.3 |
| Yemen, Rep. |
YE |
335.7 |
| Georgia |
GE |
308.3 |
| Guatemala |
GT |
305.7 |
| NA |
RE |
304.0 |
| Angola |
AO |
299.5 |
| Albania |
AL |
299.3 |
| Mali |
ML |
291.3 |
| Nicaragua |
NI |
288.3 |
| Mongolia |
MN |
287.7 |
| St. Kitts and Nevis |
KN |
276.3 |
| Rwanda |
RW |
266.0 |
| Brunei Darussalam |
BN |
250.5 |
| Congo, Dem. Rep. |
CD |
242.5 |
| Cote d'Ivoire |
CI |
235.8 |
| Sao Tome and Principe |
ST |
232.0 |
| Libya |
LY |
226.0 |
| Moldova |
MD |
207.0 |
| Grenada |
GD |
181.2 |
| Namibia |
NA |
179.3 |
| Gabon |
GA |
163.8 |
| Myanmar |
MM |
156.5 |
| Mauritius |
MU |
155.2 |
| Fiji |
FJ |
153.5 |
| NA |
GP |
153.3 |
| Barbados |
BB |
152.0 |
| Togo |
TG |
138.5 |
| Papua New Guinea |
PG |
131.3 |
| Dominican Republic |
DO |
127.3 |
| Honduras |
HN |
123.8 |
| Bahamas, The |
BS |
109.5 |
| Niger |
NE |
102.8 |
| French Polynesia |
PF |
90.5 |
| NA |
SJ |
81.3 |
| Tajikistan |
TJ |
77.0 |
| Kyrgyz Republic |
KG |
74.3 |
| Congo, Rep. |
CG |
61.8 |
| Guinea-Bissau |
GW |
57.8 |
| Guinea |
GN |
56.0 |
| Afghanistan |
AF |
55.8 |
| Korea, Dem. People's Rep. |
KP |
54.5 |
| Madagascar |
MG |
53.0 |
| Lesotho |
LS |
51.0 |
| Trinidad and Tobago |
TT |
46.0 |
| Sierra Leone |
SL |
45.0 |
| British Virgin Islands |
VG |
44.0 |
| Bermuda |
BM |
44.0 |
| Suriname |
SR |
40.0 |
| Eswatini |
SZ |
40.0 |
| Gambia, The |
GM |
39.0 |
| Bhutan |
BT |
35.0 |
| Faroe Islands |
FO |
31.8 |
| New Caledonia |
NC |
30.5 |
| Cabo Verde |
CV |
29.5 |
| Curacao |
CW |
29.0 |
| NA |
GF |
25.5 |
| Greenland |
GL |
25.5 |
| Guyana |
GY |
21.5 |
| Burundi |
BI |
19.5 |
| Mauritania |
MR |
17.0 |
| Somalia |
SO |
14.0 |
| Liechtenstein |
LI |
13.5 |
| Liberia |
LR |
12.5 |
| St. Vincent and the Grenadines |
VC |
11.0 |
| South Sudan |
SS |
10.0 |
| Sint Maarten (Dutch part) |
SX |
8.0 |
| St. Lucia |
LC |
6.5 |
| Chad |
TD |
6.5 |
| Monaco |
MC |
6.3 |
| NA |
JE |
6.0 |
| Gibraltar |
GI |
5.5 |
| Cayman Islands |
KY |
5.0 |
| Maldives |
MV |
4.5 |
| Marshall Islands |
MH |
4.5 |
| NA |
MQ |
4.0 |
| Belize |
BZ |
4.0 |
| Turkmenistan |
TM |
4.0 |
| Haiti |
HT |
4.0 |
| Macao SAR, China |
MO |
3.0 |
| Micronesia, Fed. Sts. |
FM |
3.0 |
| Central African Republic |
CF |
3.0 |
| Palau |
PW |
3.0 |
| Seychelles |
SC |
2.0 |
| Eritrea |
ER |
2.0 |
| Andorra |
AD |
2.0 |
| Timor-Leste |
TL |
2.0 |
| Aruba |
AW |
1.5 |
| Isle of Man |
IM |
1.0 |
| NA |
VA |
1.0 |
| NA |
MS |
1.0 |
| NA |
AX |
0.5 |
# average apc
average_apc <- works %>%
# first get rid of duplicates from concepts
distinct(country_code, id, work_frac, author_position, institution_id,
APC_in_dollar) %>%
group_by(country_code) %>%
# compute the average APC using fractional authorships as weights
mutate(sum_frac = sum(work_frac)) %>%
group_by(country_code, sum_frac) %>%
summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>%
collect() %>%
add_country()
## `summarise()` has grouped output by 'country_code'. You can override using the
## `.groups` argument.
# average APC over time
average_apc_time <- works %>%
# first get rid of duplicates from concepts
distinct(country_code, id, work_frac, author_position, institution_id,
APC_in_dollar, publication_year) %>%
group_by(country_code, publication_year) %>%
# compute the average APC using fractional authorships as weights
mutate(sum_frac = sum(work_frac)) %>%
group_by(country_code, sum_frac, publication_year) %>%
summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>%
collect()
## `summarise()` has grouped output by 'country_code', 'sum_frac'. You can override
## using the `.groups` argument.
average_apc_time %>%
left_join(wdi, by = c("country_code" = "iso2c")) %>%
ggplot(aes(publication_year, mean_apc)) +
geom_line(aes(group = country), alpha = .3) +
geom_smooth(se = FALSE, colour = "#007FA8") +
facet_wrap(vars(region)) +
scale_x_continuous(breaks = scales::pretty_breaks(6)) +
coord_cartesian(ylim = c(0, 3000)) +
labs(x = NULL, y = "Mean APC")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

All three joined
all_three_descriptives <- universities_per_country %>%
left_join(papers_per_country, by = c("country", "country_code")) %>%
left_join(average_apc, by = c("country", "country_code")) %>%
# remove missing values
# these arise since the wdi data on country codes does not have a mapping for
# all codes that are present in the data. in most cases, these are small
# countries, and in the case of a high university count (433) simply all
# institutions that were not assigned a country code at all.
drop_na()
all_three_descriptives %>%
arrange(desc(n_universities), desc(sum_fractional_works)) %>%
mutate(mean_apc = round(mean_apc, digits = 1),
sum_fractional_works = scales::comma(sum_fractional_works)) %>%
select(Country = country, `n universities` = n_universities,
`n fractional publications` = sum_fractional_works,
`Mean APC` = mean_apc) %>%
knitr::kable()
| United States |
6808 |
747,996.20 |
2020.0 |
| India |
2061 |
118,094.20 |
861.8 |
| China |
1985 |
595,755.70 |
1969.9 |
| United Kingdom |
1610 |
194,942.30 |
2011.3 |
| Japan |
1444 |
148,536.00 |
1881.5 |
| Germany |
1033 |
170,473.80 |
2029.1 |
| France |
1024 |
89,289.20 |
1926.6 |
| Brazil |
807 |
400,590.80 |
381.1 |
| Canada |
746 |
115,456.20 |
1900.4 |
| Russian Federation |
665 |
37,819.30 |
700.5 |
| Spain |
642 |
120,279.70 |
1285.0 |
| Italy |
582 |
104,839.50 |
1900.0 |
| Australia |
538 |
100,722.00 |
1991.2 |
| Korea, Rep. |
478 |
103,472.30 |
1816.7 |
| Indonesia |
407 |
50,570.50 |
203.2 |
| Poland |
333 |
58,718.30 |
1086.9 |
| Netherlands |
329 |
61,987.00 |
2063.3 |
| Switzerland |
293 |
42,420.70 |
2091.2 |
| Taiwan, China |
264 |
58,803.50 |
1892.1 |
| Turkiye |
240 |
32,717.70 |
880.4 |
| Pakistan |
240 |
20,774.20 |
1207.6 |
| Mexico |
236 |
40,329.80 |
931.0 |
| Iran, Islamic Rep. |
234 |
48,697.30 |
779.7 |
| Norway |
219 |
26,270.00 |
1859.5 |
| Colombia |
205 |
29,179.20 |
365.5 |
| Ukraine |
199 |
10,561.50 |
403.7 |
| Czech Republic |
195 |
20,532.30 |
1387.1 |
| Sweden |
186 |
48,538.70 |
1990.2 |
| Portugal |
181 |
27,315.50 |
1138.9 |
| Austria |
177 |
21,768.80 |
1943.7 |
| Nigeria |
171 |
13,013.30 |
934.5 |
| South Africa |
168 |
37,177.80 |
1211.7 |
| Belgium |
158 |
32,310.30 |
1916.2 |
| Thailand |
156 |
15,408.00 |
1564.1 |
| Argentina |
151 |
21,129.70 |
789.1 |
| Greece |
145 |
13,735.50 |
1603.3 |
| Finland |
144 |
19,189.30 |
1862.1 |
| Denmark |
142 |
29,004.80 |
2002.7 |
| Ireland |
138 |
12,550.70 |
1928.1 |
| Bangladesh |
132 |
6,685.30 |
979.0 |
| Philippines |
122 |
2,368.50 |
1110.9 |
| Malaysia |
121 |
36,006.30 |
1201.9 |
| Israel |
120 |
20,143.30 |
2103.5 |
| Hungary |
112 |
10,517.80 |
1642.5 |
| New Zealand |
108 |
13,046.30 |
1756.2 |
| Vietnam |
103 |
3,455.30 |
1452.4 |
| Chile |
97 |
18,898.00 |
925.9 |
| Singapore |
97 |
17,013.70 |
2075.7 |
| Egypt, Arab Rep. |
96 |
16,972.70 |
992.4 |
| Saudi Arabia |
91 |
20,301.30 |
1434.5 |
| Slovak Republic |
89 |
7,011.00 |
839.4 |
| Kenya |
86 |
3,518.50 |
1675.9 |
| Romania |
80 |
7,396.50 |
1154.0 |
| Bulgaria |
80 |
3,555.00 |
707.3 |
| Peru |
78 |
6,205.50 |
296.0 |
| Slovenia |
76 |
6,396.30 |
1233.3 |
| Ecuador |
65 |
3,926.50 |
404.3 |
| Uganda |
64 |
2,887.30 |
1898.8 |
| Serbia |
60 |
9,506.30 |
697.1 |
| United Arab Emirates |
60 |
3,175.50 |
1585.9 |
| Croatia |
59 |
7,322.80 |
675.8 |
| Ethiopia |
54 |
9,428.20 |
1686.4 |
| Ghana |
54 |
4,885.50 |
1569.8 |
| Iraq |
52 |
2,854.20 |
757.3 |
| Kazakhstan |
51 |
999.80 |
1065.0 |
| Tanzania |
48 |
2,216.70 |
1806.2 |
| Algeria |
47 |
1,059.30 |
586.5 |
| Nepal |
46 |
3,416.30 |
859.4 |
| Belarus |
40 |
668.50 |
823.3 |
| Venezuela, RB |
39 |
1,633.70 |
428.8 |
| Sri Lanka |
38 |
2,985.80 |
1337.2 |
| Latvia |
38 |
1,190.70 |
705.7 |
| Cuba |
38 |
1,097.30 |
734.6 |
| Lithuania |
36 |
3,961.00 |
1223.8 |
| Estonia |
33 |
2,875.00 |
1342.4 |
| Tunisia |
33 |
2,727.00 |
1236.8 |
| Jordan |
32 |
2,812.80 |
1331.7 |
| Morocco |
28 |
5,027.80 |
1730.6 |
| Zimbabwe |
28 |
1,017.70 |
1243.5 |
| Uruguay |
27 |
2,814.80 |
654.1 |
| Cameroon |
27 |
2,285.50 |
1630.5 |
| Sudan |
27 |
897.50 |
1445.8 |
| Uzbekistan |
27 |
467.00 |
537.9 |
| Lebanon |
26 |
2,848.50 |
1615.8 |
| Cyprus |
26 |
1,630.20 |
1506.0 |
| Costa Rica |
25 |
3,380.80 |
230.2 |
| Armenia |
23 |
1,364.00 |
1231.6 |
| Luxembourg |
23 |
1,142.50 |
1998.6 |
| Georgia |
22 |
308.30 |
1177.3 |
| Oman |
21 |
1,593.00 |
645.0 |
| Cambodia |
20 |
623.50 |
1248.0 |
| Bolivia |
20 |
368.30 |
359.0 |
| Albania |
20 |
299.30 |
721.8 |
| Qatar |
19 |
2,813.70 |
1525.4 |
| Kuwait |
19 |
1,584.20 |
1561.7 |
| Bosnia and Herzegovina |
19 |
1,202.70 |
432.0 |
| Iceland |
19 |
1,110.30 |
1617.8 |
| Syrian Arab Republic |
19 |
551.80 |
987.2 |
| Azerbaijan |
19 |
383.30 |
637.8 |
| Myanmar |
19 |
156.50 |
816.9 |
| Congo, Dem. Rep. |
18 |
242.50 |
1597.4 |
| West Bank and Gaza |
17 |
879.70 |
1524.6 |
| Paraguay |
17 |
634.50 |
124.7 |
| Zambia |
16 |
706.80 |
1683.8 |
| Malawi |
15 |
553.00 |
1939.3 |
| Panama |
15 |
550.80 |
1551.3 |
| Dominican Republic |
15 |
127.30 |
786.4 |
| Mozambique |
14 |
1,257.30 |
432.3 |
| Senegal |
14 |
480.50 |
1294.7 |
| Yemen, Rep. |
14 |
335.70 |
1132.6 |
| Mongolia |
14 |
287.70 |
719.8 |
| Moldova |
14 |
207.00 |
1016.1 |
| Kyrgyz Republic |
14 |
74.30 |
1226.4 |
| Bahrain |
13 |
4,004.80 |
1727.7 |
| North Macedonia |
13 |
375.80 |
604.3 |
| Burkina Faso |
13 |
370.00 |
1867.9 |
| Guatemala |
13 |
305.70 |
700.8 |
| Rwanda |
12 |
266.00 |
1712.2 |
| Hong Kong SAR, China |
11 |
1,261.20 |
1957.9 |
| Botswana |
10 |
450.00 |
1019.1 |
| Cote d'Ivoire |
10 |
235.80 |
1294.0 |
| Libya |
10 |
226.00 |
887.8 |
| Afghanistan |
10 |
55.80 |
967.3 |
| El Salvador |
9 |
566.00 |
52.6 |
| Nicaragua |
9 |
288.30 |
210.6 |
| Tajikistan |
9 |
77.00 |
405.7 |
| Trinidad and Tobago |
8 |
46.00 |
925.9 |
| Somalia |
8 |
14.00 |
1131.0 |
| Angola |
7 |
299.50 |
191.4 |
| Namibia |
7 |
179.30 |
1173.9 |
| Papua New Guinea |
7 |
131.30 |
1562.2 |
| Madagascar |
7 |
53.00 |
1411.7 |
| Malta |
6 |
506.00 |
1247.0 |
| Mali |
6 |
291.30 |
2015.2 |
| Mauritius |
6 |
155.20 |
1010.4 |
| Niger |
6 |
102.80 |
1669.3 |
| Benin |
5 |
1,381.80 |
1816.5 |
| Jamaica |
5 |
881.00 |
1466.1 |
| Lao PDR |
5 |
621.50 |
1684.1 |
| Kosovo |
5 |
422.50 |
777.0 |
| Montenegro |
5 |
365.30 |
548.9 |
| Fiji |
5 |
153.50 |
1293.4 |
| Honduras |
5 |
123.80 |
931.0 |
| Gambia, The |
5 |
39.00 |
1821.5 |
| Brunei Darussalam |
4 |
250.50 |
1016.9 |
| Gabon |
4 |
163.80 |
2012.9 |
| Togo |
4 |
138.50 |
1203.7 |
| Korea, Dem. People's Rep. |
4 |
54.50 |
677.1 |
| Curacao |
4 |
29.00 |
1487.7 |
| South Sudan |
4 |
10.00 |
1751.0 |
| Puerto Rico |
3 |
958.50 |
1902.2 |
| St. Kitts and Nevis |
3 |
276.30 |
2036.7 |
| Bahamas, The |
3 |
109.50 |
177.7 |
| French Polynesia |
3 |
90.50 |
1612.2 |
| Guinea-Bissau |
3 |
57.80 |
2056.2 |
| Sierra Leone |
3 |
45.00 |
1817.6 |
| Bermuda |
3 |
44.00 |
1818.2 |
| Greenland |
3 |
25.50 |
987.3 |
| Mauritania |
3 |
17.00 |
2185.8 |
| Liechtenstein |
3 |
13.50 |
1165.2 |
| Liberia |
3 |
12.50 |
2094.9 |
| Monaco |
3 |
6.30 |
1318.7 |
| Maldives |
3 |
4.50 |
967.8 |
| Belize |
3 |
4.00 |
613.8 |
| Turkmenistan |
3 |
4.00 |
589.4 |
| Antigua and Barbuda |
2 |
881.70 |
800.9 |
| Congo, Rep. |
2 |
61.80 |
1625.9 |
| Suriname |
2 |
40.00 |
1865.9 |
| Eswatini |
2 |
40.00 |
1202.7 |
| Bhutan |
2 |
35.00 |
351.1 |
| New Caledonia |
2 |
30.50 |
1660.5 |
| Guyana |
2 |
21.50 |
881.7 |
| Burundi |
2 |
19.50 |
1057.2 |
| Sint Maarten (Dutch part) |
2 |
8.00 |
777.5 |
| St. Lucia |
2 |
6.50 |
1480.5 |
| Chad |
2 |
6.50 |
1613.7 |
| Haiti |
2 |
4.00 |
1300.2 |
| Macao SAR, China |
2 |
3.00 |
0.0 |
| Seychelles |
2 |
2.00 |
1607.5 |
| Sao Tome and Principe |
1 |
232.00 |
330.4 |
| Grenada |
1 |
181.20 |
1335.3 |
| Barbados |
1 |
152.00 |
1200.3 |
| Guinea |
1 |
56.00 |
1695.7 |
| Lesotho |
1 |
51.00 |
663.4 |
| British Virgin Islands |
1 |
44.00 |
1203.6 |
| Faroe Islands |
1 |
31.80 |
1406.3 |
| Cabo Verde |
1 |
29.50 |
435.9 |
| St. Vincent and the Grenadines |
1 |
11.00 |
922.7 |
| Gibraltar |
1 |
5.50 |
1199.1 |
| Cayman Islands |
1 |
5.00 |
416.6 |
| Marshall Islands |
1 |
4.50 |
532.8 |
| Palau |
1 |
3.00 |
1849.5 |
| Micronesia, Fed. Sts. |
1 |
3.00 |
942.7 |
| Central African Republic |
1 |
3.00 |
1391.7 |
| Timor-Leste |
1 |
2.00 |
1245.0 |
| Andorra |
1 |
2.00 |
1350.4 |
| Eritrea |
1 |
2.00 |
387.5 |
| Aruba |
1 |
1.50 |
1616.0 |
| Isle of Man |
1 |
1.00 |
2235.4 |
# restrict data for plotting so we only plot countries with at least 5 universities
all_three_descriptives <- all_three_descriptives %>%
filter(n_universities >= 5)
gdp <- WDI::WDI(start = 2019, end = 2019)
# plot n papers against average apc
p <- all_three_descriptives %>%
left_join(wdi, by = c("country_code" = "iso2c", "country" = "country")) %>%
ggplot(aes(sum_fractional_works, mean_apc, colour = region, label = country)) +
geom_point() +
scale_x_log10(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "top") +
labs(y = NULL, colour = NULL, x = "Sum of fractional publications")
p

plotly::ggplotly(p)
pdata <- all_three_descriptives %>%
left_join(gdp, by = c("country_code" = "iso2c", "country" = "country")) %>%
left_join(wdi, by = c("country_code" = "iso2c", "country" = "country"))
labels <- pdata %>%
mutate(label = case_when(
country %in% c("China", "India", "United States","Uganda",
"Brazil", "Switzerland", "Israel", "Spain",
"Saudi Arabia") ~ country,
TRUE ~ ""))
p <- pdata %>%
ggplot(aes(NY.GDP.PCAP.KD, mean_apc, colour = region, label = country)) +
geom_point(aes(alpha = sum_fractional_works)) +
ggrepel::geom_text_repel(data = labels, aes(label = label),
show.legend = FALSE, max.overlaps = Inf,
box.padding = 1, min.segment.length = 0,
nudge_y = -10) +
scale_x_continuous(labels = scales::dollar) +
scale_y_continuous(labels = scales::dollar) +
scale_alpha_continuous(trans = "log10", range = c(.1, 1),
labels = scales::comma) +
scale_colour_discrete_qualitative(palette = "Dark 3") +
theme(legend.position = "top", legend.box = "vertical") +
labs(y = "Mean APC", colour = NULL, x = "GDP per capita",
alpha = "Number of fractional publications")
p

plotly::ggplotly(p)
Across topics within continents
papers_per_country_per_field <- works %>%
mutate(total_weight = work_frac * concept_frac) %>%
distinct(country, country_code, id, author_position, institution_id, field,
total_weight) %>%
group_by(country, country_code, field) %>%
summarise(sum_fractional_works = sum(total_weight) %>% round(digits = 1)) %>%
arrange(desc(sum_fractional_works)) %>%
collect()
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
plot_data <- papers_per_country_per_field %>%
left_join(wdi, by = c("country_code" = "iso2c")) %>%
group_by(region, field) %>%
summarise(sum_fractional_works = sum(sum_fractional_works)) %>%
mutate(prop = sum_fractional_works / sum(sum_fractional_works),
label = case_when(
prop > .15 ~ scales::percent(prop, accuracy = .1),
TRUE ~ "")
)
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
plot_data %>%
drop_na() %>%
ggplot(aes(prop, fct_reorder(field, prop))) +
geom_segment(aes(xend = 0, yend = field), colour = "grey70") +
geom_point() +
geom_text(aes(label = label), nudge_x = .015, hjust = "left") +
facet_wrap(vars(region)) +
scale_x_continuous(expand = expansion(mult = c(0.05, .25)),
labels = function(x) scales::percent(x, accuracy = 1)) +
labs(x = "% of fractional publications", y = NULL) +
theme(panel.grid = element_blank(),
panel.grid.major.x = element_line(colour = "grey92"),
axis.text.y = element_text(margin = margin(r = -2)))

spark_disconnect(sc)